Abstract
In the United States, school mobility is relatively common and often linked to negative academic outcomes, yet little is known about its role in non-Western contexts with more standardized educational systems. Does the relationship between school mobility and academic achievement differ in contexts where schools are more uniform in curriculum, instruction, and academic expectations? This study addresses this question by examining the determinants and consequences of school mobility in South Korea, a country characterized by a highly standardized education system. Using nationally representative longitudinal data on fifth-grade students, this study finds that only 3.2% of students transferred schools between Grades 5 and 6—the final two years of elementary school in South Korea—including 0.7% who transferred for strategic reasons and 2.5% for nonstrategic reasons. Students from non-intact families were more likely to transfer for non-strategic reasons, whereas socioeconomically advantaged students were more likely to move strategically. However, school mobility had no significant association with achievement gains after accounting for covariates, regardless of transfer motivation. These findings suggest that while the motivations underlying school mobility may be similar across countries, its academic consequences are shaped by institutional contexts rather than representing a universal pattern.
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Keywords: School Mobility; Academic Achievement; Longitudinal Research; Standardized Educational Systems; South Korea
Introduction
School mobility—defined as any non-promotional school change—is a common feature of American students’ educational trajectories (
Rumberger, 2015;
Swanson & Schneider, 1999;
U.S. Government Accountability Office [GAO], 2010;
Welsh, 2017). For example,
U.S. GAO (2010) reports that about 70% of students in a cohort of kindergarteners (1998–2007) changed schools two times or fewer before high school. Although debate continues over whether school mobility is beneficial or harmful (see
Welsh, 2017), a substantial body of U.S.-based research links it to negative educational outcomes (e.g.,
Alexander et al., 1996;
Astone & McLanahan, 1994;
Gasper et al., 2012;
Grigg, 2012;
Pribesh & Downey, 1999;
Rumberger & Larson, 1998;
South & Haynie, 2004;
Tucker et al., 1998;
Voight et al., 2012). A key explanation for this negative association is that school changes often create mismatches between students’ prior and new school contexts, including differences in curriculum, instruction, and academic standards (
Long, 1975;
Rumberger & Larson, 1998;
Voight et al., 2012). Mobile students may also struggle to form social ties in new environments, hindering their adjustment (
Coleman, 1988;
Gasper et al., 2012;
Pribesh & Downey, 1999;
South & Haynie, 2004;
Swanson & Schneider, 1999). As a result, they tend to perform worse academically than their nonmobile peers.
While these explanations highlight mechanisms linking school mobility to lower achievement, they pay less attention to how system-level features shape this relationship. In the United States, substantial variation in curriculum and academic standards across schools (
Schneider et al., 1998;
Sutton et al., 2013) means that transferring students often face discontinuities in instruction and greater adjustment challenges. Reflecting this,
Hanushek et al. (2004) proposed “more standardized curricula” (p. 1744) to mitigate the negative effects of mobility. This raises an important question: does the association between school mobility and academic achievement differ in contexts with more standardized and uniform school systems? Addressing this question within the United States is difficult given its highly decentralized and variable education system (
Buchmann, 2011). A useful alternative is to examine school mobility in contexts where curricula and academic expectations are more standardized across schools. International comparative research can thus illuminate when and why school mobility is more or less consequential for academic achievement. Yet, evidence from such contexts, especially non-Western settings, remains limited.
This study addresses this gap by examining school mobility in South Korea (hereafter Korea), a context with a highly standardized education system in sharp contrast to the decentralized U.S. system (
Park, 2008;
Park et al., 2011). Most Korean elementary students—the focus of this study—follow a common curriculum in relatively uniform school environments, so transfers involve minimal curricular disruption (
Byun et al., 2021;
Byun et al., 2012a;
Byun et al., 2012b). In addition, widespread private tutoring (e.g., hagwon) provides opportunities for students to catch up and maintain performance (
Byun, 2014;
Byun et al., 2012b;
Park et al., 2011). Accordingly, one might expect the negative effects of school mobility to be relatively modest in the Korean context. Using nationally representative longitudinal data, this study tests this expectation and contributes to the literature by providing evidence from a non-Western and highly standardized educational system.
Theories and Hypothesis
Consequences of School Mobility
Most research on school mobility—largely based in the United States—focuses on its determinants and consequences (see
Reynolds et al., 2009;
Welsh, 2017 for reviews). This literature consistently documents pronounced socioeconomic and racial/ethnic disparities, with mobility more common among disadvantaged and minority students (
Welsh, 2017). Age also matters, as younger children tend to move more frequently (
Reynolds et al., 2009). These patterns highlight the importance of accounting for background characteristics when assessing mobility’s academic effects. However, evidence on the determinants of school mobility outside the United States remains limited.
On the other hand, research on the effects of school mobility in the United States is typically guided by two theoretical perspectives (
Welsh, 2017). The first, rooted in
Bronfenbrenner’s (1989) ecological theory, emphasizes that mobility disrupts students’ adaptation to new environments—teachers, peers, norms, and expectations—thereby interrupting academic and social continuity (
Reynolds et al., 2009;
Temple & Reynolds, 1999;
Voight et al., 2012). The second draws on
Coleman’s (1988) theory of social capital, highlighting how school changes weaken ties with peers and teachers, reducing engagement and sense of belonging (
Gasper et al., 2012;
Pribesh & Downey, 1999;
South & Haynie, 2004;
Swanson & Schneider, 1999) and increasing the risk of academic underachievement.
A substantial body of U.S.-based empirical research supports these theoretical perspectives (e.g.,
Alexander et al., 1996;
Astone & McLanahan, 1994;
Gasper et al., 2012;
Grigg, 2012;
Herbers et al., 2013;
Pribesh & Downey, 1999;
Rumberger & Larson, 1998;
South & Haynie, 2004;
Sutton et al., 2013;
Tucker et al., 1998;
Voight et al., 2012). Synthesizing 26 studies published between 1975 and 1994,
Mehana and Reynolds (2004) found a consistent negative association between school mobility and academic achievement. Similarly,
Reynolds et al. (2009) reported that 13 of 16 studies published between 1990 and 2008 documented negative links between mobility and educational outcomes.
More recent evidence, however, presents a more nuanced picture. Reviewing 73 studies published between 1994 and 2014,
Welsh (2017) concludes that the effects of school mobility are inconclusive. Some studies (e.g.,
Alexander et al., 1996) find that negative associations disappear once prior academic achievement and background characteristics are controlled. Others (e.g.,
Hanushek et al., 2004) even identify potential benefits. Drawing on the concept of “Tiebout” mobility, where families move to access better schools,
Hanushek and colleagues (2004) report small but significant gains in mathematics achievement among students who changed districts, although within-district moves often have negative effects, especially for disadvantaged and minority students (see also
Swanson & Schneider, 1999;
Temple & Reynolds, 1999).
Together, these findings underscore the importance of considering why students change schools. Yet most prior studies rely on a simple dichotomy—whether a student moved—without distinguishing voluntary from involuntary or strategic from non-strategic transfers. Evidence outside the United States is also limited. One exception is
Strand and Demie (2006,
2007) in England: their study of secondary schools (2007) found negative effects even after controls, whereas their earlier work on elementary students (2006) showed that these associations were largely explained by background characteristics. Overall, while U.S. research often reports negative links between mobility and achievement, the evidence is less consistent than previously assumed, and cross-national evidence remains scarce. Moreover, variation in the motives for mobility has been largely overlooked.
To examine the role of school mobility in Korea, the current study draws on comparative research highlighting how national education systems shape students’ experiences and outcomes (e.g.,
Buchmann & Dalton, 2002;
Buchmann & Park, 2009;
Kerckhoff, 1995,
2001;
Park, 2008;
Shavit et al., 2007;
Van de Werfhorst et al., 2010). This literature suggests that many U.S.-based findings reflect the country’s distinctive institutional features—particularly its decentralization, strong local control, and limited standardization (
Buchmann, 2011). For example, using the Programme for International Student Assessment 2000 data,
Park (2008) showed that parent–child communication has weaker effects on academic achievement among low-socioeconomic status (SES) families in less standardized systems like the United States than in highly standardized systems like Korea. In decentralized systems, low-SES students face greater difficulty navigating variation in curricula, standards, and extracurricular options. By contrast, standardized systems offer greater consistency in instruction and assessment, allowing even disadvantaged parents to more effectively monitor and support their children’s schooling.
These insights help contextualize U.S.-based findings on the negative effects of school mobility. From an ecological perspective, mobility disrupts learning by creating discontinuities between schools—a problem likely amplified in decentralized systems with variable curricula and standards. From a social capital perspective, mobility weakens ties with teachers and peers, an effect that may be stronger in the United States, where families rely more on informal networks to navigate school quality (
Park, 2008). Taken together, these arguments suggest that the negative effects of mobility observed in the United States may be less pronounced in more standardized systems, such as Korea, where students experience greater curricular and institutional consistency.
Cross-national research also highlights differences in how families support learning outside school. In East Asian contexts such as Korea and Hong Kong, families invest heavily in private supplementary education known as shadow education (
Baker et al., 2001;
Bray, 1999;
Byun, 2014;
Byun et al., 2018;
Park et al., 2011). These resources may buffer disruptions from school changes, potentially weakening the negative academic effects of mobility. Although private tutoring has expanded in the United States (
Buchmann et al., 2010), it remains far less prevalent than in East Asia (
Byun et al., 2018). As a result, the adverse effects of mobility observed in the United States may be less applicable in contexts like Korea, where shadow education can mitigate such risks. In sum, the negative association between school mobility and achievement documented in the United States may reflect institutional and cultural features specific to that context and may not generalize to Korea.
Korea is relatively small—about 100,000 square kilometers (roughly 39,000 square miles), slightly smaller than Pennsylvania—yet densely populated, with about 50 million residents as of 2025 (
Central Intelligence Agency, 2025). Its well-developed transportation infrastructure, including highways and high-speed rail, allows travel from Seoul (the capital city in the northern part of the country) to Busan (the second-largest city in the south) in about five hours by car or three hours by high-speed train. As a result, families may be less likely to relocate for employment and instead commute from their current residence, reducing the need for school transfers. Consistent with this, Statistics Korea reports that only about 3% of K–12 students changed schools in 2020 (
Korean Statistical Information Service, 2025).
This does not mean, however, that school transfers are absent or entirely random in the Korean context. Although overall rates of school mobility remain relatively low compared with countries such as the United States, some student transfers still occur for reasons unrelated to education, including parental job changes, residential relocation, family restructuring, or other life-course transitions. At the same time, some families—particularly those from higher socioeconomic backgrounds—may strategically pursue access to schools perceived to offer superior academic environments, more advantaged peer groups, stronger reputations, or pathways to selective secondary and tertiary education, a pattern consistent with Tiebout-style mobility. This is because the perceptions of school quality are often shaped less by objective indicators of school effectiveness than by social reputation, parental networks, and subjective beliefs about desirable educational environments. Taken together, these patterns suggest that school mobility in Korea may be driven by both non-educational constraints and educationally motivated choices, consistent with patterns observed in other countries such as the United States.
Yet, several institutional features of Korean education may reduce academic consequences of school mobility, regardless of the underlying transfer motivation, particularly at the elementary level examined here. First, while only a small share of students (about 1.5%) attend private schools, most are randomly assigned to public schools based on residential location (
Byun et al., 2021). Among public elementary schools, variation in resources, curriculum, and teacher quality is minimal, reflecting Korea’s egalitarian approach (
Byun et al., 2012a,
2012b). As a result, students experience largely uniform curricula and learning environments (
Byun et al., 2021). This stands in sharp contrast to the United States, where substantial between-school differences in resources and curriculum are common (
Sutton et al., 2013;
Swanson & Schneider, 1999).
In addition, students typically remain in the same homeroom with a stable group of classmates, which may facilitate rapid social integration for new arrivals. By contrast, U.S. students often rotate across classes and peer groups, making it harder to form relationships. Moreover, the high level of standardization and transparency in Korea enables parents to monitor their children’s progress more directly, reducing reliance on informal information networks involving school personnel and other parents (
Park, 2008). Finally, although instructional pace may vary somewhat across schools, widespread participation in private supplementary education provides opportunities for students to catch up. In 2025, for example, 84.4% of Korean elementary students received private tutoring in core subjects such as mathematics and English (
Statistics Korea, 2026).
In sum, Korea’s nationally standardized curriculum tends to result in limited variation in instructional content and expectations across schools. Students, teachers, and schools also tend to share similar academic goals, fostering a common school climate centered on achievement. In addition, widespread access to private tutoring may provide students with opportunities to compensate for any gaps following a school change. Together, these features likely reduce the impact of school mobility, regardless of the underlying transfer motivation.
Hypothesis: The academic consequences of school mobility are expected to be limited in Korea’s highly standardized educational system, regardless of transfer motivation.
Two Korean studies have examined the effects of school mobility on academic achievement. Using the Seoul Child Panel Study,
Kim and Moon (2012) found that mobility between Grades 4–5 and 5–6 negatively affected subsequent mathematics scores. Similarly, using the Korean Child and Adolescent Panel Study,
Cho and Kim (2019) reported negative effects on mathematics and English achievement. While their findings are broadly consistent with much of the U.S. literature, these studies have several important limitations. First, both rely on a simple dichotomous measure of mobility, without distinguishing strategic from non-strategic transfers. Second, neither adequately captures achievement change over time:
Kim and Moon (2012) did not control for prior academic achievement, and
Cho and Kim (2019) used self-reported rather than standardized measures. Thus, the effects of school mobility on academic performance in Korea remain inconclusive and warrant further investigation.
Data and Methods
This study used longitudinal data from the Korean Education Longitudinal Study 2013 (KELS:2013), which began tracking a nationally representative cohort of more than 7,000 fifth-grade students in 2013 through annual follow-up surveys. KELS:2013 employed a two-stage stratified sampling design. First, elementary schools were selected proportional to size across four regional categories (Seoul, metropolitan areas, mid- and small-sized cities, and rural areas). Second, students were randomly sampled within selected schools. The baseline survey (2013) included 7,287 students from 242 schools, with a participation rate of 99.5%. The first follow-up survey (2014) retained approximately 98% of the original sample. The analytic sample for the present study included students with valid information on both academic achievement and school mobility across the two waves (N = 6,950).
It is important to note that this study focuses specifically on school mobility between Grades 5 (baseline) and 6 (first follow-up), which correspond to the final two years of elementary school in Korea. This focus is theoretically and methodologically important because school mobility, by definition, refers to non-promotional school changes. Extending the analysis beyond Grade 6 would largely capture normative transitions from elementary school (Grades 1–6) to middle school (Grades 7–9), and from middle school to high school (Grades 10–12), rather than voluntary or non-routine school transfers. As such, these later transitions primarily reflect structurally mandated educational progression rather than the type of school mobility examined in the present study.
In addition, as noted above, the original KELS:2013 sampling design was based on 242 elementary schools and their students. Once students advanced to middle school, however, they became dispersed across 1,125 middle schools, with only a small number of students enrolling in the same school. This dispersion substantially complicates the use of school fixed-effects models that integrate both elementary and middle school contexts. In this regard, KELS:2013 is particularly well suited for examining the effects of school mobility between Grades 5 and 6 within a relatively stable elementary school setting.
Key Variables
Academic achievement gains. The primary outcomes were achievement gains in English and mathematics, which are widely regarded as the two most important academic subjects in Korea and the subjects for which the majority of elementary school students participate in private tutoring (
Byun et al., 2021). As part of KELS:2013, standardized assessments in both subjects were administered to all students—including those who transferred schools—in both Grades 5 and 6. Achievement scores were estimated using item response theory (IRT) scaling. Gain scores were then calculated as the difference between students’ sixth-grade and fifth-grade scores. To facilitate interpretation, the gain scores were standardized to have a mean of 0 and a standard deviation of 1. This standardization allows the estimated effects of school mobility to be interpreted as effect sizes, thereby enabling more straightforward comparisons across models and outcomes.
School mobility. Mobility status was coded as a three-category variable: (1) no mobility, (2) nonstrategic mobility, and (3) strategic mobility. This classification was based on parent reports of whether the child transferred schools between Grades 5 and 6 and the reason for the transfer. Parents who reported a move selected one primary reason: (a) parental job transfer or relocation, (b) pursuit of a higher-quality educational environment (e.g., teachers, curriculum, facilities, or access to private tutoring), (c) voluntary transfer due to difficulties at the previous school, (d) school-recommended transfer, or (e) other reasons. Transfers motivated by educational quality (Category b) and voluntary transfers due to difficulties at the previous school (Category c) were classified as strategic school mobility, whereas all other transfer reasons were coded as non-strategic school mobility.
1
It is important to note that, as described later, only 3.2% of fifth-grade students (n = 220) changed schools between Grades 5 and 6. Of these students, 0.7% (n = 49) transferred for strategic reasons, whereas 2.5% (n = 171) transferred for non-strategic reasons. The strategic mobility group was therefore quite small, and this limited sample size may reduce statistical power and make it more difficult to detect statistically significant differences. Accordingly, null findings for strategic mobility should not be interpreted as definitive evidence of no effect. At the same time, however, combining these students with other forms of non-strategic mobility into a single overall mobility category would reproduce an important limitation of much of the prior literature: the tendency to treat qualitatively distinct forms of school mobility as homogeneous. After carefully considering this trade-off, I concluded that distinguishing strategic from non-strategic mobility was theoretically and substantively more important, despite the reduced statistical power associated with the smaller subgroup. Nevertheless, the findings for the strategic mobility group should be interpreted with appropriate caution given the limited sample size.
Covariates. Following prior research, I controlled for a range of family, student, and school characteristics associated with school mobility and academic achievement. Due to space constraints, I describe them briefly here and provide detailed measures in
Appendix Table A. Family variables included parental education, logged monthly income, family structure, number of siblings, parental educational expectations, plans for selective high school enrollment, and school satisfaction (standardized composite). Student characteristics included gender, study time, private tutoring, educational expectations, prior school mobility, and prior academic achievement. School variables included sector and location. All covariates were measured at baseline, prior to any school transfer.
Analytic Strategies
I began with descriptive statistics by mobility status, followed by multinomial regression models predicting school mobility. To account for students nested within schools, I used cluster-robust standard errors, which adjust for within-school correlation and reduce the risk of Type I error (
Rogers, 1993). To estimate the association between school mobility and achievement gains, I employed school fixed-effects models, adjusting for unobserved school and neighborhood factors. Missing data on covariates (see
Appendix Table A for missingness rates) were addressed using multiple imputations under the missing-at-random assumption (
Schafer & Graham, 2002). All variables were included in the imputation model (
Johnson & Young, 2011), and cases with missing dependent variables were excluded from analysis (
von Hippel, 2007). I generated 25 imputed datasets using chained equations (
Royston & White, 2011) to ensure stable estimates, and combined results using
Rubin’s (1987) rules.
Results
Table 1 presents descriptive statistics by school mobility status. Overall, 3.2% of students changed schools between Grades 5 and 6, including 0.7% who transferred for strategic reasons and 2.5% who transferred for non-strategic reasons. In terms of academic achievement gains, there were no statistically significant differences in either English or mathematics across school mobility groups. In terms of family background, students who transferred for strategic reasons were more socioeconomically advantaged than both non-mobile students and those who moved for nonstrategic reasons. For example, their parents had higher average years of schooling (M = 15.93, SD = 2.23) than those of non-mobile students (M = 14.46, SD = 2.32) and non-strategic movers (M = 14.59, SD = 2.38). They were also more likely to have parents who planned to enroll them in selective high schools (47%, compared with 28% and 27% among the no-mobility and non-strategic mobility groups, respectively).
Regarding student characteristics, students who transferred for strategic reasons (M = 0.26, SD = 1.02) demonstrated higher mathematics achievement than non-strategic movers (M = −0.14, SD = 1.03). In contrast, students who transferred for non-strategic reasons were less likely to participate in private tutoring (71%) than non-mobile students (80%) or strategic movers (92%). In terms of school characteristics, only 1% of non-strategic movers attended private schools, compared with 5% of non-mobile students and 4% of strategic movers. There were no statistically significant differences in school location across school mobility groups.
Predictors of School Mobility
Table 2 presents results from multinomial logistic regression models predicting school mobility, with non-mobile students as the reference group. For non-strategic transfers, significant predictors include family structure, prior school mobility, and school sector. Students from intact (two-parent) families are less likely to transfer (b = –0.658, SE = 0.226, p < .01), whereas those with prior school mobility are more likely to do so (b = 0.874, SE = 0.168, p < .001). Attending a private school substantially reduces the likelihood of non-strategic transfers (b = –2.265, SE = 0.897, p < .05). Students in mid- and small-sized cities are also less likely to transfer than those in rural areas (b = – 0.519, SE = 0.239, p < .05). For strategic transfers, parental education (b = 0.174, SE = 0.065, p < .01) and family income (b = 0.719, SE = 0.252, p < .01) are significant, indicating that higher-SES students are more likely to move strategically. However, private school attendance lowers this likelihood (b = –1.283, SE = 0.637, p < .05). Students in mid- and small-sized cities are also less likely to transfer strategically than those in rural areas (b = –.926, SE = 0.415, p < .05). Overall, these findings reveal distinct socioeconomic and educational profiles for students who transfer for strategic versus non-strategic reasons.
The Relationship between School Mobility and Academic Gains
Table 3 presents results from school fixed-effects models predicting achievement gains. Students who transferred for strategic reasons scored, on average, approximately 0.163 standard deviations higher in English and 0.114 standard deviations higher in mathematics than their non-mobile peers, net of covariates; however, these differences were not statistically significant. Similarly, non-strategic transfers show no significant differences from non-mobile students in either subject, suggesting no clear academic disadvantage. Prior school mobility also has no significant association with achievement gains.
Before turning to the broader implications, I briefly examine covariates that are significantly associated with achievement gains. Higher parental education, intact family structure, and stronger parental expectations predicted greater gains. Female students outperformed males, and private tutoring was positively associated with gains in both subjects. Students’ own educational expectations were also positively related to achievement gains. In contrast, higher prior academic achievement was associated with smaller subsequent gains.
Discussion
Much is known about the academic consequences of school mobility in the United States, but far less is understood in other contexts. To address this research gap, this study examined school mobility in Korea, where a highly standardized education system contrasts sharply with the more decentralized U.S. system. U.S.-based research often attributes the negative effects of mobility to mismatches in curriculum and academic standards across schools. In Korea, however, greater curricular consistency may reduce such disruptions and ease adjustment for transferring students. I tested this hypothesis using nationally representative data on Korean elementary school students.
My analyses showed that school mobility was relatively rare in Korea. Only 3.2% of fifth graders changed schools between Grades 5 and 6, including 0.7% for strategic reasons and 2.5% for non-strategic reasons—figures consistent with national statistics (
Korean Statistical Information Service, 2025). In addition, about 26% of students had experienced school mobility prior to fifth grade. Taken together, these findings suggest that school mobility is less common in Korea than in the United States. Korea’s small geographic size and limited between-school variation may reduce the need to relocate schools. At the same time, in a highly competitive educational environment, parents may be reluctant to move their children due to concerns about disrupting academic progress and weakening their competitive position.
Regarding the characteristics of mobile students in Korea, students from non-intact families were more likely to transfer for non-strategic reasons, even after controlling for other background factors. This pattern aligns with U.S. research (
Astone & McLanahan, 1994;
Burkam et al., 2009;
Rumberger & Larson, 1998;
U.S. GAO, 2010), which shows that family disruptions, such as divorce, increase the likelihood of school mobility. In contrast, socioeconomically advantaged students were more likely to transfer for strategic reasons. This finding is also consistent with U.S. evidence (
Alexander et al., 1996;
Hanushek et al., 2004) on Tiebout mobility, whereby families relocate to access schools perceived to offer higher quality or better opportunities.
Regarding the impact of school mobility, I found no significant association between school mobility and achievement gains after controlling for covariates, regardless of the underlying motivations for school transfers. In other words, Korean elementary students who changed schools experienced neither clear academic advantages nor disadvantages. This finding supports my hypothesis and is in line with evidence from
Alexander et al. (1996), which shows no significant effects once background factors are considered. However, this finding contrasts with prior studies in Korea (
Cho & Kim, 2019;
Kim & Moon, 2012) and much of the U.S. literature (e.g.,
Astone & McLanahan, 1994;
Grigg, 2012;
Pribesh & Downey, 1999;
Rumberger & Larson, 1998;
South & Haynie, 2004;
Sutton et al., 2013), which generally report negative effects of mobility.
I highlighted several institutional features of Korean education—namely a standardized curriculum, uniform school climate and expectations, and widespread access to private tutoring—that may explain the absence of significant mobility effects on achievement gains. As in the U.S., students who change schools may face adjustment challenges. However, in Korea, these disruptions are likely smaller because students may encounter relatively consistent curricula, standards, and expectations across schools. In addition, extensive participation in private tutoring may provide opportunities to compensate for any gaps following a transfer. Taken together, these findings suggest that the consequences of school mobility may be institutionally contingent rather than universal.
However, the present study does not directly examine the mechanisms through which school mobility may (or may not) affect academic gains. Accordingly, the interpretations offered here should be understood as suggestive rather than causal. In addition, the findings should not be interpreted to mean that greater educational standardization or expanded participation in private tutoring is inherently desirable or would necessarily mitigate the potential negative effects of school mobility. Rather, the results highlight the importance of considering institutional and contextual factors when assessing the consequences of school mobility. Nor do the findings provide definitive conclusions about school mobility in Korea or elsewhere. Although this study offers new empirical evidence on the relationship between school mobility and academic achievement in the Korean context, further research is needed to clarify the mechanisms underlying these associations and to better understand the motivations, processes, and consequences of school mobility across different educational contexts.
This study has several additional limitations that should be addressed in future research. First, because of the nature of the data used, the present study examined school mobility only between Grades 5 and 6 and therefore captures only the short-term effects of mobility on academic achievement. Future research should adopt longer longitudinal time frames to examine the potential long-term consequences of school mobility. Such research may be particularly important in the Korean context, where upper secondary education is highly stratified and differentiated by school type (
Byun & Park, 2017). Given the distinct structure of Korean upper secondary education, additional research is needed to better understand how the consequences of school mobility may vary across different stages and institutional contexts of schooling.
Second, the present study focused primarily on the consequences of school mobility for academic achievement gains. Future research, however, should also examine a broader range of non-cognitive outcomes, including peer relationships, school belonging, and other socio-emotional dimensions of students’ school experiences. As discussed earlier, one of the major theoretical perspectives on school mobility in the United States is
Coleman’s (1988) theory of social capital, which emphasizes that changing schools may weaken students’ relationships with peers and teachers and thereby reduce their sense of connectedness and engagement. The limited body of research on school mobility in the Korean context, including the present study, has primarily focused on academic outcomes. Much less is known about how school mobility shapes students’ social relationships, peer networks, and sense of school belonging in Korea. In this regard, future research is needed to examine the socio-emotional and relational consequences of school mobility, including its effects on peer relationships and school belonging. In short, examining school mobility across different grade levels and a broader range of outcomes would provide a more comprehensive understanding of its consequences in the Korean context.
Despite these limitations, this study offers broader implications for research on school mobility beyond Korea. It highlights institutional features—particularly the high degree of standardization in curriculum and school climate—as potential explanations for the absence of significant mobility effects. In addition, widespread access to private tutoring may help offset learning disruptions associated with school changes. If these factors indeed buffer the negative consequences of mobility, similar patterns may emerge in other East Asian contexts with comparable systems. Further research using cross-national approaches is needed to directly examine the roles of a standardized curriculum and widespread private tutoring in shaping the consequences of school mobility and to test these explanations more rigorously.
In fact, recent years have seen a rapid global expansion of private supplementary tutoring (
Buchmann, 2011;
Park et al., 2016). At the same time, some countries, including Korea, have pursued decentralization and reduced curriculum standardization to enhance accountability (
Park, 2008), while others, such as the United States, have moved toward greater standardization to improve achievement (
Shepard et al., 2009). These shifts—both across and within countries—underscore the need to examine not only cross-national differences in the relationship between school mobility and educational outcomes, but also how this relationship changes over time within countries. Comparative research on these dynamics can provide important insights into how evolving institutional contexts shape the consequences of school mobility. I hope this study serves as a useful starting point for such work.
Notes
Table 1.Descriptive Statistics of the Variables Included in Analyses by School Mobility
Table 1.
|
Variable |
No mobility (a)
|
Non-strategic mobility (b)
|
Strategic mobility (c)
|
Total
|
|
M |
|
SD |
M |
|
SD |
M |
|
SD |
M |
SD |
|
Outcomes (test gain scores)
|
|
|
|
|
|
|
|
|
|
|
|
|
English |
0.00 |
|
1.00 |
0.05 |
|
1.00 |
0.15 |
|
0.90 |
0.00 |
1.00 |
|
Mathematics |
0.00 |
|
1.00 |
0.06 |
|
1.09 |
0.08 |
|
0.91 |
0.00 |
1.00 |
|
Covariates
|
|
|
|
|
|
|
|
|
|
|
|
|
Family background
|
|
|
|
|
|
|
|
|
|
|
|
|
Parental education |
14.46 |
c
|
2.32 |
14.59 |
c
|
2.38 |
15.93 |
a, b
|
2.23 |
14.47 |
2.32 |
|
Monthly family income (log) |
5.99 |
c
|
0.60 |
5.92 |
c
|
0.68 |
6.35 |
a, b
|
0.57 |
5.99 |
0.60 |
|
Two-parent family |
0.91 |
b
|
— |
0.82 |
a
|
— |
0.95 |
|
— |
0.90 |
— |
|
Number of siblings |
1.20 |
|
0.78 |
1.34 |
|
0.92 |
1.15 |
|
0.59 |
1.20 |
0.79 |
|
Parental educational expectations for their child |
|
|
|
|
|
|
|
|
|
|
|
|
Two-year college degree or less |
0.07 |
|
— |
0.04 |
|
— |
0.04 |
|
— |
0.07 |
— |
|
Four-year college degree |
0.59 |
c
|
— |
0.61 |
c
|
— |
0.41 |
a. b
|
— |
0.59 |
— |
|
Master’s degree |
0.10 |
|
— |
0.11 |
|
— |
0.19 |
|
— |
0.10 |
— |
|
Doctoral degree |
0.20 |
|
— |
0.17 |
|
— |
0.30 |
|
— |
0.20 |
— |
|
Don’t know |
0.04 |
|
— |
0.07 |
|
— |
0.06 |
|
— |
0.04 |
— |
|
Parents' plans for selective high schools |
0.28 |
c
|
— |
0.27 |
c
|
— |
0.47 |
a, b
|
— |
0.28 |
— |
|
School satisfaction |
0.00 |
|
1.00 |
-0.06 |
|
0.91 |
0.18 |
|
1.12 |
0.00 |
1.00 |
|
N
|
6,730 |
171 |
49 |
6,950 |
|
(%)
|
(96.8) |
(2.5) |
(0.7) |
(100.0) |
|
Student characteristics
|
|
|
|
|
|
|
|
|
|
|
|
|
Female |
0.51 |
|
— |
0.48 |
|
— |
0.49 |
|
— |
0.51 |
— |
|
Time spent studying |
1.96 |
|
1.58 |
2.01 |
|
1.58 |
2.20 |
|
1.61 |
1.96 |
1.58 |
|
Private tutoring |
0.80 |
b
|
— |
0.71 |
a, c
|
— |
0.92 |
b
|
— |
0.80 |
— |
|
Student educational expectations |
|
|
|
|
|
|
|
|
|
|
|
|
Two-year college degree or less |
0.11 |
|
— |
0.10 |
|
— |
0.11 |
|
— |
0.11 |
— |
|
Four-year college degree |
0.40 |
|
— |
0.37 |
|
— |
0.36 |
|
— |
0.40 |
— |
|
Master’s degree |
0.08 |
|
— |
0.11 |
|
— |
0.12 |
|
— |
0.08 |
— |
|
Doctoral degree |
0.11 |
|
— |
0.10 |
|
— |
0.21 |
|
— |
0.11 |
— |
|
Don’t know |
0.30 |
|
— |
0.32 |
|
— |
0.21 |
|
— |
0.30 |
— |
|
Prior school mobility |
0.25 |
b
|
— |
0.46 |
a
|
— |
0.34 |
|
— |
0.26 |
— |
|
Prior academic achievement |
|
|
|
|
|
|
|
|
|
|
|
|
English |
0.00 |
|
1.00 |
-0.09 |
|
1.02 |
0.24 |
|
1.09 |
0.00 |
1.00 |
|
Mathematics |
0.01 |
|
0.99 |
-0.14 |
c
|
1.03 |
0.26 |
b
|
1.02 |
0.00 |
0.99 |
|
School characteristics
|
|
|
|
|
|
|
|
|
|
|
|
|
Private school |
0.05 |
b
|
— |
0.01 |
a, c
|
— |
0.04 |
b
|
— |
0.05 |
— |
|
Location |
|
|
|
|
|
|
|
|
|
|
|
|
Seoul |
0.18 |
|
— |
0.14 |
|
— |
0.16 |
|
— |
0.18 |
— |
|
Metro |
0.24 |
|
— |
0.25 |
|
— |
0.35 |
|
— |
0.24 |
— |
|
City |
0.38 |
|
— |
0.34 |
|
— |
0.24 |
|
— |
0.38 |
— |
|
Rural |
0.20 |
|
— |
0.27 |
|
— |
0.24 |
|
— |
0.20 |
— |
|
N
|
6,730 |
171 |
49 |
6,950 |
|
(%)
|
(96.8) |
(2.5) |
(0.7) |
(100.0) |
Table 2.Results from the Multinomial Logistic Regression Model Predicting School Change between Fifth and Sixth Grades
Table 2.
|
Variable |
Non-strategic school change
|
Strategic school change
|
|
b
|
SE
|
b
|
SE
|
|
Family background
|
|
|
|
|
|
Parental education |
0.057 |
0.042 |
0.174**
|
0.065 |
|
Family income |
0.057 |
0.171 |
0.719**
|
0.252 |
|
Two-parent family |
-0.658**
|
0.226 |
0.309 |
0.749 |
|
Number of siblings |
0.157 |
0.082 |
0.015 |
0.163 |
|
Parental educational expectations for their child |
|
|
|
|
|
Two-year college degree or less (Ref.) |
— |
— |
— |
— |
|
Four-year college degree |
0.722 |
0.385 |
-0.502 |
0.667 |
|
Master’s degree |
0.746 |
0.392 |
-0.040 |
0.709 |
|
Doctoral degree |
0.453 |
0.413 |
-0.447 |
0.735 |
|
Don’t know |
0.898 |
0.493 |
0.334 |
0.856 |
|
Parents’ plans for enrolling their child in selective high schools |
0.092 |
0.190 |
0.508 |
0.362 |
|
School satisfaction |
-0.055 |
0.082 |
0.068 |
0.161 |
|
Student characteristics
|
|
|
|
|
|
Female |
-0.106 |
0.159 |
0.037 |
0.291 |
|
Time spent studying |
0.052 |
0.046 |
0.005 |
0.088 |
|
Private tutoring |
-0.372 |
0.210 |
0.758 |
0.625 |
|
Student educational expectations |
|
|
|
|
|
Two-year college degree or less (Ref.) |
— |
— |
— |
— |
|
Four-year college degree |
0.133 |
0.290 |
-0.082 |
0.526 |
|
Master’s degree |
0.502 |
0.334 |
0.058 |
0.555 |
|
Doctoral degree |
0.146 |
0.378 |
0.319 |
0.530 |
|
Don’t know |
0.172 |
0.296 |
-0.299 |
0.560 |
|
Prior school mobility |
0.874***
|
0.168 |
0.256 |
0.341 |
|
Prior academic achievement |
-0.074 |
0.111 |
-0.121 |
0.210 |
|
School characteristics
|
|
|
|
|
|
Private school |
-2.265*
|
0.897 |
-1.283*
|
0.637 |
|
Location |
|
|
|
|
|
Seoul |
-0.515 |
0.335 |
-0.881 |
0.584 |
|
Metro |
-0.371 |
0.259 |
-0.155 |
0.356 |
|
City |
-0.519*
|
0.239 |
-0.926*
|
0.415 |
|
Rural (Ref.) |
— |
— |
— |
— |
|
Constant |
-4.980***
|
1.063 |
-12.310***
|
2.050 |
|
Log likelihooda
|
-1030.341 |
|
Pseudo R2a
|
0.057 |
|
N |
6,950 |
Table 3.School-Fixed Effects Estimates of the Effect of School Mobility on Academic Achievement Gains
Table 3.
|
Fixed-effect |
English
|
Mathematics
|
|
b
|
SE
|
b
|
SE
|
|
School mobility |
|
|
|
|
|
No school change (Ref.) |
— |
— |
— |
— |
|
Non-strategic school mobility |
0.039 |
0.070 |
0.022 |
0.069 |
|
Strategic school mobility |
0.163 |
0.128 |
0.114 |
0.127 |
|
Covariates |
|
|
|
|
|
Family background
|
|
|
|
|
|
Parental education |
0.030***
|
0.006 |
0.018**
|
0.006 |
|
Family income |
0.057*
|
0.023 |
-0.010 |
0.023 |
|
Two-parent family |
0.163***
|
0.040 |
0.086*
|
0.039 |
|
Number of siblings |
-0.044**
|
0.014 |
-0.016 |
0.014 |
|
Parental educational expectations for their child |
|
|
|
|
|
Two-year college degree or less (Ref.) |
— |
— |
— |
— |
|
Four-year college degree |
0.151**
|
0.046 |
0.145**
|
0.046 |
|
Master’s degree |
0.186**
|
0.058 |
0.256***
|
0.058 |
|
Doctoral degree |
0.165**
|
0.053 |
0.211***
|
0.053 |
|
Don’t know |
0.147*
|
0.067 |
0.106 |
0.066 |
|
Parents’ plans for selective high schools |
0.225***
|
0.027 |
0.173***
|
0.027 |
|
School satisfaction |
0.007 |
0.011 |
0.011 |
0.011 |
|
Student characteristics
|
|
|
|
|
|
Female |
0.176***
|
0.022 |
0.089***
|
0.021 |
|
Time spent studying |
0.019**
|
0.007 |
-0.012 |
0.007 |
|
Private tutoring |
0.075*
|
0.030 |
0.084**
|
0.030 |
|
Student educational expectations |
|
|
|
|
|
Two-year college degree or less (Ref.) |
— |
— |
— |
— |
|
Four-year college degree |
0.054 |
0.037 |
0.065 |
0.036 |
|
Master’s degree |
0.123*
|
0.051 |
0.106*
|
0.050 |
|
Doctoral degree |
0.161**
|
0.048 |
0.124**
|
0.047 |
|
Don’t know |
0.006 |
0.037 |
0.001 |
0.037 |
|
Prior school mobility |
-0.039 |
0.026 |
-0.041 |
0.026 |
|
Prior academic achievement |
-0.492***
|
0.013 |
-0.469***
|
0.012 |
|
Constant |
-1.299***
|
0.146 |
-0.600***
|
0.142 |
|
R-squareda
|
|
|
|
|
|
Within model |
0.174 |
0.180 |
|
Between model |
0.186 |
0.189 |
|
Overall model |
0.168 |
0.182 |
|
Random effecta
|
|
|
|
|
|
Student-level variance (σ2e) |
0.763 |
0.751 |
|
School-level variance (σ2u) |
0.104 |
0.117 |
|
Rho (ρ) |
0.120 |
0.135 |
|
N |
|
|
|
|
|
Student |
6,950 |
|
School |
239 |
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Appendix
Appendix
Table A. The Description of the Variables Included in Analyses
Table
|
Covariate |
Description |
% of imputed cases |
|
Family background
|
|
|
|
Parental education |
Highest years of schooling completed by either parent (e.g., 16 = four-year college) |
1.3 |
|
Family income |
Parent-reported monthly household income, log-transformed using the natural logarithm |
4.5 |
|
Family structure |
Parental report of whether both biological parents reside with the surveyed student (0 = no, 1 = yes) |
3.4 |
|
Number of siblings |
Parent-reported count of the student’s siblings |
1.1 |
|
Parental educational expectations for their child |
Highest level of education parents expect their child to complete |
1.7 |
|
Parents’ plans for selective high schools |
Parental report of whether parents planned to enroll their child in a special-purpose or autonomous high school (0 = no, 1 = yes) |
2.2 |
|
School satisfaction |
A standardized composite score based on parents’ responses to the following items, rated on a 5-point scale (1 = very unsatisfied, 5 = very satisfied): (1) improvement of basic skills, (2) education tailored to the child’s level, (3) fair student evaluation by teachers, (4) career guidance based on aptitude, (5) character education, (6) school facilities that support comfortable learning, (7) opportunities to participate in school events or educational activities, and (8) harmonious relationships between teachers and parent |
1.9 |
|
Student characteristics
|
|
|
|
Gender |
Self-reported sex (0 = male, 1 = female) |
0.0 |
|
Time spent studying |
Self-reported average hours per week the student reports studying alone |
1.0 |
|
Private tutoring |
Student report of participation in private supplementary education (0 = no, 1 = yes) |
13.2 |
|
Student educational expectations |
Highest level of education the student expects to complete |
0.6 |
|
Prior school mobility |
Parent-reported indicator of whether the child had ever transferred schools after enrolling in elementary school and prior to Grade 5. |
0.8 |
|
Prior academic achievement |
Fifth-grade test scores in English and mathematics, estimated using IRT scaling and standardized to have a mean of 0 and a standard deviation of 1. |
0.0 |
|
School characteristics
|
|
|
|
Private school |
Public (= 0) and private (= 1) |
0.0 |
|
Location |
School location: Seoul, other metropolitan cities, mid- and small-sized cities, and rural areas |
0.0 |
